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Recent Advances in Spine Surgery- Pros and Cons

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Journal of Clinical Orthopaedics | Vol 8 | Issue 2 |  Jul-Dec 2023 | page: 50-53 | Arvind Janardhan Vatkar, Sumedha Shinde, Sachin Kale, Pramod Bhor

DOI: https://doi.org/10.13107/jcorth.2023.v08i02.596

Authors: Arvind Janardhan Vatkar [1], Sumedha Shinde [2], Sachin Kale [3], Pramod Bhor [4]

[1] Consultant Orthopedic Spine Surgeon, Fortis Hiranandani Hospital, Vashi, Navi Mumbai- 400703, India,
[2] Assistant Professor, Sir JJ Blood Center, Byculla, Mumbai, Maharashtra 400008, India,
[3] Department of Orthopedics, D Y Patil Hospital and medical college, Ayyappa Temple Road, Dr D Y Patil
Vidyanagar, Sector 5, Nerul, Navi Mumbai, Maharashtra 400706, India,
[4] Director of Department of Orthopaedics, Fortis Hiranandani Hospital, Vashi, Navi Mumbai- 400703, India.

Address of Correspondence
Dr. Arvind Vatkar Janardhan,
Department of Orthopaedics, Fortis Hiranandani hospital, Juhu Nagar, Sector 10A, Vashi, Navi Mumbai,
Maharashtra 400703, India.
E-mail: vatkararvind@gmail.com

Abstract

Recent innovations in spine surgery, such as minimally invasive spine surgery, endoscopic spine surgery, robotic spine surgery, virtual reality, artificial intelligence, and biomaterials, have resulted in significant gains, although more work need to be done. Minimally invasive and endoscopic spine operations provide the advantages of minimum tissue stress and early recovery, but they have a high learning curve. Robotic spine surgery and virtual reality can improve precision and planning, although they are expensive and may be restricted in availability. Artificial intelligence and biomaterials show promise for personalised therapy, but their long-term implications are still being investigated. To make educated judgments for their patients, surgeons must consider the benefits and drawbacks of various technologies.
Keywords: Minimally invasive spine surgery, Artificial intelligence, Robotic spine surgery, Endoscopic spine surgery, Virtual reality, Biomaterials

References

1. Móga K, Ferencz A, Haidegger T. What is next in computer-assisted spine surgery? Advances in image-guided robotics and extended reality. Robotics 2022;12:1.
2. Zhou S, Zhou F, Sun Y, Chen X, Diao Y, Zhao Y, et al. The application of artificial intelligence in spine surgery. Front Surg 2022;9:885599.
3. Choi JY, Park SM, Kim HJ, Yeom JS. Recent updates on minimally invasive spine surgery: Techniques, technologies, and indications. Asian Spine J 2022;16:1013-21.
4. Sharma A, Shakya A, Singh V, Deepak P, Mangale N, Jaiswal A, et al. Incidence of dural tears in open versus minimally invasive spine surgery: A Single-center prospective study. Asian Spine J 2022;16:463-70.
5. Wang X, Borgman B, Vertuani S, Nilsson J. A systematic literature review of time to return to work and narcotic use after lumbar spinal fusion using minimal invasive and open surgery techniques. BMC Health Serv Res 2017;17:446.
6. Thongtrangan I, Le H, Park J, Kim DH. Minimally invasive spinal surgery: A historical perspective. Neurosurg Focus 2004;16:E13.
7. Tieber F, Lewandrowski KU. Technology advancements in spinal endoscopy for staged management of painful spine conditions. J Spine Surg 2020;6:S19-28.
8. Park SM, Park J, Jang HS, Heo YW, Han H, Kim HJ, et al. Biportal endoscopic versus microscopic lumbar decompressive laminectomy in patients with spinal stenosis: A randomized controlled trial. Spine J 2020;20:156-65.
9. Kang MS, You KH, Han SY, Park SM, Choi JY, Park HJ. Percutaneous full-endoscopic versus biportal endoscopic posterior cervical foraminotomy for unilateral cervical foraminal disc disease. Clin Orthop Surg 2022;14:539-47.
10. Fiani B, Siddiqi I, Reardon T, Sarhadi K, Newhouse A, Gilliland B, et al. Thoracic endoscopic spine surgery: A comprehensive review. Int J Spine Surg 2020;14:762-71.
11. Naros G, Machetanz K, Grimm F, Roser F, Gharabaghi A, Tatagiba M. Framed and non-framed robotics in neurosurgery: A 10-year single-center experience. Int J Med Robot 2021;17:e2282.
12. Grimm F, Naros G, Gutenberg A, Keric N, Giese A, Gharabaghi A. Blurring the boundaries between frame-based and frameless stereotaxy: Feasibility study for brain biopsies performed with the use of a head-mounted robot. J Neurosurg 2015;123:737-42.
13. Fatima N, Massaad E, Hadzipasic M, Shankar GM, Shin JH. Safety and accuracy of robot-assisted placement of pedicle screws compared to conventional free-hand technique: A systematic review and meta-analysis. Spine J 2021;21:181-92.
14. Han X, Tian W, Liu Y, Liu B, He D, Sun Y, et al. Safety and accuracy of robot-assisted versus fluoroscopy-assisted pedicle screw insertion in thoracolumbar spinal surgery: A prospective randomized controlled trial. J Neurosurg Spine 2019;??? ;1-8.
15. Keric N, Doenitz C, Haj A, Rachwal-Czyzewicz I, Renovanz M, Wesp DM, et al. Evaluation of robot-guided minimally invasive implantation of 2067 pedicle screws. Neurosurg Focus 2017;42:E11.
16. Park J, Ham DW, Kwon BT, Park SM, Kim HJ, Yeom JS. Minimally invasive spine surgery: Techniques, technologies, and indications. Asian Spine J 2020;14:694-701.
17. Yu Y, Li ZZ, Nishimura Y. Editorial: Endoscopic spine surgery. Front Surg 2022;9:1127851.
18. Hasan S, Miller A, Higginbotham D, Saleh ES, McCarty S. Virtual and augmented reality in spine surgery: An era of immersive healthcare. Cureus 2023;15:e43964.
19. Gasco J, Patel A, Ortega-Barnett J, Branch D, Desai S, Kuo YF, et al. Virtual reality spine surgery simulation: An empirical study of its usefulness. Neurol Res 2014;36:968-73.
20. Ma L, Fan Z, Ning G, Zhang X, Liao H. 3D Visualization and augmented reality for orthopedics. Adv Exp Med Biol 2018;1093:193-205.
21. Hasan LK, Haratian A, Kim M, Bolia IK, Weber AE, Petrigliano FA. Virtual reality in orthopedic surgery training. Adv Med Educ Pract 2021;12:1295-301.
22. Saeed SA, Masters RM. Disparities in health care and the digital divide. Curr Psychiatry Rep 2021;23:61.
23. Browd SR, Park C, Donoho DA. Potential applications of artificial intelligence and machine learning in spine surgery across the continuum of care. Int J Spine Surg 2023;17:S26-33.
24. Broida SE, Schrum ML, Yoon E, Sweeney AP, Dhruv NN, Gombolay MC, et al. Improving surgical triage in spine clinic: Predicting likelihood of surgery using machine learning. World Neurosurg 2022;163:e192-8.
25. Wilson B, Gaonkar B, Yoo B, Salehi B, Attiah M, Villaroman D, et al. Predicting spinal surgery candidacy from imaging data using machine learning. Neurosurgery 2021;89:116-21.
26. Xie N, Wilson PJ, Reddy R. Use of machine learning to model surgical decision-making in lumbar spine surgery. Eur Spine J 2022;31:2000-6.
27. Peng L, Zhang G, Zuo H, Lan L, Zhou X. Surgical design optimization of proximal junctional kyphosis. J Healthc Eng 2020;2020:8886599.
28. Garcia-Canadilla P, Isabel-Roquero A, Aurensanz-Clemente E, Valls-Esteve A, Miguel FA, Ormazabal D, et al. Machine learning-based systems for the anticipation of adverse events after pediatric cardiac surgery. Front Pediatr 2022;10:930913.
29. Iqbal FM, Joshi M, Fox R, Koutsoukou T, Sharma A, Wright M, et al. Outcomes of vital sign monitoring of an acute surgical cohort with wearable sensors and digital alerting systems: A pragmatically designed cohort study and propensity-matched analysis. Front Bioeng Biotechnol 2022;10:895973.
30. Boaro A, Leung J, Reeder HT, Siddi F, Mezzalira E, Liu G, et al. Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures. J Neurosurg Spine 2021;35:796-806.
31. Lyman S, Hidaka C, Fields K, Islam W, Mayman D. Monitoring patient recovery after THA or TKA using mobile technology. HSS J 2020;16:358-65.
32. Shi LY, Wang A, Zang FZ, Wang JX, Pan XW, Chen HJ. Tantalum-coated pedicle screws enhance implant integration. Colloids Surf B Biointerfaces 2017;160:22-32.
33. Takayanagi A, Siddiqi I, Ghanchi H, Lischalk J, Vrionis F, Ratliff J, et al. Radiolucent carbon fiber-reinforced implants for treatment of spinal tumors-clinical, radiographic, and dosimetric considerations. World Neurosurg 2021;152:61-70.
34. Lv ZT, Xu Y, Cao B, Dai J, Zhang SY, Huang JM, et al. Titanium-coated PEEK versus uncoated PEEK cages in lumbar interbody fusion: A systematic review and meta-analysis of randomized controlled trial. Clin Spine Surg 2023;36:198-209.
35. Singhatanadgige W, Tangchitcharoen N, Kerr SJ, Tanasansomboon T, Yingsakmongkol W, Kotheeranurak V, et al. A comparison of polyetheretherketone and titanium-coated polyetheretherketone in minimally invasive transforaminal lumbar interbody fusion: A randomized clinical trial. World Neurosurg 2022;168:e471-9.
36. Feng JT, Yang XG, Wang F, He X, Hu YC. Efficacy and safety of bone substitutes in lumbar spinal fusion: A systematic review and network meta-analysis of randomized controlled trials. Eur Spine J 2020;29:1261-76.
37. Iunes EA, Barletta EA, Barba Belsuzarri TA, Onishi FJ, Cavalheiro S, Joaquim AF. Correlation between different interbody grafts and pseudarthrosis after anterior cervical discectomy and fusion compared with control group: Systematic review. World Neurosurg 2020;134:272-9.
38. Ament JD, Vokshoor A, Yee R, Johnson JP. A systematic review and meta-analysis of silicon nitride and biomaterial modulus as it relates to subsidence risk in spinal fusion surgery. N Am Spine Soc J 2022;12:100168.
39. Fiani B, Jarrah R, Shields J, Sekhon M. Enhanced biomaterials: Systematic review of alternatives to supplement spine fusion including silicon nitride, bioactive glass, amino peptide bone graft, and tantalum. Neurosurg Focus 2021;50:E10.
40. Cottrill E, Premananthan C, Pennington Z, Ehresman J, Theodore N, Sciubba DM, et al. Radiographic and clinical outcomes of silicate-substituted calcium phosphate (SiCaP) bone grafts in spinal fusion: Systematic review and meta-analysis. J Clin Neurosci 2020;81:353-66.
41. Choi UY, Kim KT, Kim KG, Lim SH, Kim YJ, Sohn S, et al. Safety and tolerability of stromal vascular fraction combined with β-Tricalcium phosphate in posterior lumbar interbody fusion: Phase I clinical trial. Cells 2020;9:??? .
42. Kwon BT, Kim HJ, Lee S, Park SM, Ham DW, Park HJ, et al. Feasibility and safety of a CaO-SiO2-P2O5-B2O3 bioactive glass ceramic spacer in posterior lumbar interbody fusion compared with polyetheretherketone cage: A prospective randomized controlled trial. Acta Neurochir 2023;165:135-44.

How to Cite this article: Vatkar AJ, Shinde S, Kale S, Bhor P. Recent Advances in Spine Surgery- Pros and Cons. Journal of Clinical Orthopaedics 2023;8(2):50-53.

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ORTHO AI: The Dawn of a New Era: Artificial Intelligence in Orthopaedics

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Journal of Clinical Orthopaedics | Vol 8 | Issue 2 |  Jul-Dec 2023 | page: 05-06 | Parag Sancheti, Neeraj Bijlani, Ashok Shyam, Amit Yerudkar, Rohan Lunawat

DOI: https://doi.org/10.13107/jcorth.2023.v08i02.578

Authors: Parag Sancheti [1], Neeraj Bijlani [2], Ashok Shyam [1, 3], Amit Yerudkar [4], Rohan Lunawat [4]

[1] Sancheti Institute for Orthopaedics and Rehabilitation, Pune, Maharashtra India.

[2] Orthotech Clinic, Chembur, Mumbai, India.

[3] ndian Orthopaedic Research group, Thane, India.

[4] Scriptlanes Pvt Ltd, Pune, India.

Address of Correspondence
Dr. Ashok Shyam, Indian Orthopaedic Research group, Thane, India.

E-mail: drashokshyam@gmail.com

Editorial

OrthoAI is the new buzz word in Orthopaedics. It was launched on 11th Dec 2023 at the hands of Dr Raghunath Mashelkar. It was an amazing event structured around the World’s First AI Focussed on Orthopaedics. Artificial Intelligence revolution began in Nov 2022 with advent of Chat GPT 3.5 on the AI landscape. Since then the advancements in Artificial Intelligence (AI) have started to change the landscape of various industries, and orthopaedics is no exception. Based on our observations and research, we firmly believe AI will redefine the landscape of orthopaedics in the near future specially in fields mentioned below but not restricted to it. With OrthoAI as first step we have created the foundation on which we can build on. We would be working in this field and follow it as it unfolds and present our insights from time to time.

OrthoAI: A Generative AI Revolution in Orthopaedics
As said OrthoAI is the foundation of the AI Revolution in Orthopaedics, and we would like to share some more insights about it. OrthoAI primary is a Generative AI like chat GPT. You can even call it OrthoGPT (Ortho Generative Pre-trained Transformer), a type of artificial intelligence model designed for understanding and generating human-like text. OrthoAI, akin to a specialized version of Chat GPT, or ‘OrthoGPT’, stands on three foundational pillars:
1. The OrthoAI Database: The essence of OrthoAI lies in its meticulously curated vector database, developed over nine months by a team of expert orthopaedic surgeons. This database is the cornerstone of OrthoAI’s reliability and validation.
2. PubMed Access: OrthoAI’s integration with PubMed enriches its evidence base, ensuring that its responses are grounded in the latest orthopaedic literature.
3. OrthoTV Access: With over eleven thousand orthopaedic videos, OrthoTV provides a wealth of expert knowledge and experiences. OrthoAI’s access to this repository allows it to offer comprehensive answers, supplemented with relevant article links and video content
Before we explore the far-reaching applications of OrthoAI, it’s crucial to gain an understanding of what AI is and its main types. At its core, AI involves creating computer systems that mimic human intelligence, enabling them to perform tasks that usually require human intellect.
Before delving into OrthoAI’s applications, it’s vital to understand AI’s core components:

• Machine Learning (ML) is a subset of AI that allows computers to learn and improve from experience without being explicitly programmed. In simpler terms, it’s like teaching a child to identify objects. Over time, the child learns to recognize and distinguish objects on their own, much like ML algorithms learn from data and improve over time. OrthoAI is primed with Machine Learning
• Deep Learning (DL) is a more complex subset of ML that mimics the neural networks of our brain. DL involves feeding a computer system a vast amount of data, which it uses to make decisions. For example, DL can help computers identify diseases by analyzing thousands of radiographs or MRI Scans. This ability to process and analyse a significant amount of data makes DL particularly valuable for OrthoAI.
• Natural Language Processing (NLP) is another critical aspect of AI that focuses on the interaction between computers and humans using natural language. NLP can read and understand human language, enabling it to extract essential information from clinical notes or respond to patient queries in real-time. OrthoAI has NLP built in and will continue to learn from all its interactions.
The rise of NLP and Large language models in last few months has infused new life in the AI arena and has piked the interest in these models.

We believe AI will primarily impact in following Five areas of Orthopaedics
OrthoAI & Clinical Decision Making

OrthoAI as discussed above is built on a validated database, along with PubMed and OrthoTV access. This makes the answers most relevant and trustworthy. With a synthesised answer from all sources, it will be source of having a huge thinking knowledge bank at your fingertips. It will act as CO-PILOT or as EBM Assistant to Orthopaedic Surgeons, helping them in tricky situations. The queries can vary from clinical, academic, case based, surgical steps, drug interactions, rehabilitation, complications, and surgical planning too. This is the version 1, and more things would be added in coming versions

AI and Orthopaedic Imaging
One of the most promising applications of AI in orthopaedics is in imaging. We rely heavily on imaging for diagnosis and treatment planning. AI can help streamline this process. ML algorithms, with their ability to identify patterns, can analyse and interpret imaging data from radiographs, MRIs, CT scans, etc. These algorithms can diagnose conditions like fractures, disc herniations and osteoarthritis with a level of accuracy comparable to experienced radiologists, saving significant diagnostic time. We believe radiologist are already using such algorithms and DL models, but it will soon extrapolate into a more common use by orthopaedic surgeons too

AI in Surgical Planning and Execution
The influence of AI will also transform surgical planning and execution. Patient specific planning and preoperative templating can be easily done by AI. AI’s influence isn’t confined to diagnostics—it’s also transforming surgical planning and execution. Additionally, AI can help surgeons place implants accurately during orthopaedic procedures, thus reducing complications and improving patient outcomes. With large data available from the rise of robotic surgeries in last decade, will help in training these models and soon a combination of robotic AI will be available to help us. OrthoAI is currently being worked as a platform for surgical planning too

AI in Prosthetics and Rehabilitation
Customised prosthetic development and suggesting innovative strategies in prosthetics that mimic natural movements will be achieve through advanced machine learning algorithms. Furthermore, AI-based rehabilitation programs will offer personalized therapy plans and monitor patient progress in real-time, ensuring a more efficient recovery process. Customisation & utilisation of smart devices in this area will increase.

AI in Predictive Analytics
Predictive analytics is another area where AI is showing tremendous potential. AI algorithms can analyse vast amounts of patient data to identify risk factors and predict disease progression. This information allows orthopaedic professionals to intervene early, potentially preventing the development of severe conditions and improving patient care. The algorithmic nature of these analysis make it most suitable to be plugged in into OrthoAI. A separate team in working on adding this unique feature to OrthoAI and we are sure that this will come as an update soon.

Ethical Considerations and Challenges
Like any technology even AI has its limitations. The issues of hallucinations of AI and providing wrong information are not uncommonly reported. This is the reason why we started with creating a validated database for OrthoAI and also limit its access to all unnecessary information. The integration of AI into orthopaedics will not be without challenges but we have created a foundation for it. AI will also raise critical ethical considerations concerning data privacy, informed consent, and accountability in the event of AI errors. Additionally, we need to develop a standardized set of regulations for using AI in healthcare to ensure its ethical and safe application.

Conclusion
OrthoAI is launched at the threshold of a new era in orthopaedics, bringing significant improvements in diagnosis, treatment planning, surgical execution, prosthetics, rehabilitation, and predictive analytics. As we start to incorporate AI more comprehensively into our practice, we need to conscientiously navigate the ethical and legal challenges it presents. Our enthusiasm as technologically inclined orthopaedic surgeons drives us to harness OrthoAI’s full potential, ushering in an era of precision, efficiency, and patient-centric care in orthopaedics.

 

How to Cite this article: Sancheti P, Bijlani N, Shyam A, Yerudkar A, Lunawat R. ORTHO AI: The Dawn of a New Era: Artificial Intelligence in Orthopaedics. Journal of Clinical Orthopaedics 2023;8(2):05-06.

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